Agentic AI Engineering with Python — Build Autonomous Agents Training Course

Agentic AI Core Concepts

• Defining autonomous agents: terminology and classification
• The agent loop: the perceive, decide, act, and observe cycle
• Patterns for defining agent responsibilities and operational scope

Python Ecosystem and Agent SDKs

• Leveraging LangChain and comparable SDKs to initialize agents
• Asynchronous programming, task queues, and subprocess oversight
• Software packaging, virtual environments, and reproducible dev workflows

External Tool and API Integration

• Architecting tool interfaces and secure invocation strategies
• Connecting to web APIs, databases, and proprietary internal services
• Governing credentials, secrets, and least-privilege access models

Memory, State, and Context Governance

• Managing short-term context windows and refining prompt engineering
• Long-term memory frameworks: Redis, vector stores, and retrieval augmentation
• Ensuring consistency, implementing caching, and maintaining memory hygiene

Orchestration, Planning, and Complex Workflows

• Chaining actions, utilizing subagents, and decomposing tasks
• Comparing planning algorithms with heuristic orchestration approaches
• Managing failures, implementing retries, and defining compensating actions

Safety, Testing, and Observability

• Developing threat models, red-teaming, and input/output sanitization
• Executing unit, integration, and end-to-end tests for agent behavior
• Implementing logging, metrics, tracing, and alerting systems

Deployment, Scaling, and Agent MLOps

• Containerization, CI/CD pipelines, and strategic rollout methods
• Cost management, rate limiting, and resource efficiency optimization
• Establishing monitoring, governance, and operational playbooks

Conclusion and Future Directions